Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China.
Sci Rep. 2020 Aug 17;10(1):13883. doi: 10.1038/s41598-020-69076-x.
Evaluating the performance of photoplethysmogram (PPG) event detection algorithms requires a large number of PPG signals with different noise levels and sampling frequencies. As publicly available PPG databases provide few options, artificially constructed PPG signals can also be used to facilitate this evaluation. Here, we propose a dynamic model to synthesize PPG over specified time durations and sampling frequencies. In this model, a single pulse was simulated by two Gaussian functions. Additionally, the beat-to-beat intervals were simulated using a normal distribution with a specific mean value and a specific standard deviation value. To add periodicity and to generate a complete signal, the circular motion principle was used. We synthesized three classes of pulses by emulating three different templates: excellent (systolic and diastolic waves are salient), acceptable (systolic and diastolic waves are not salient), and unfit (systolic and diastolic waves are noisy). The optimized model fitting of the Gaussian functions to the templates yielded 0.99, 0.98, and 0.85 correlations between the template and synthetic pulses for the excellent, acceptable, and unfit classes, respectively, with mean square errors of 0.001, 0.003, and 0.017, respectively. By comparing the heart rate variability of real PPG and randomly synthesized PPG for 5 min in 116 records from the MIMIC III database, strong correlations were found in SDNN, RMSSD, LF, HF, SD1, and SD2 (0.99, 0.89, 0.84, 0.89, 0.90 and 0.95, respectively).
评估光电容积脉搏波 (PPG) 事件检测算法的性能需要大量具有不同噪声水平和采样频率的 PPG 信号。由于公开可用的 PPG 数据库提供的选项较少，因此也可以使用人工构建的 PPG 信号来促进这种评估。在这里，我们提出了一种动态模型，用于在指定的时间段和采样频率下合成 PPG。在该模型中，单个脉搏由两个高斯函数模拟。此外，通过使用具有特定平均值和特定标准差值的正态分布来模拟心动周期间隔。为了添加周期性并生成完整的信号，使用圆形运动原理。我们通过模拟三个不同的模板来合成三类脉冲：优秀（收缩期和舒张期波明显）、可接受（收缩期和舒张期波不明显）和不合适（收缩期和舒张期波有噪声）。对模板进行高斯函数的优化模型拟合，对于优秀、可接受和不合适的三类，模板和合成脉冲之间的相关性分别为 0.99、0.98 和 0.85，均方误差分别为 0.001、0.003 和 0.017。通过比较真实 PPG 和从 MIMIC III 数据库的 116 个记录中随机合成的 PPG 5 分钟的心率变异性，在 SDNN、RMSSD、LF、HF、SD1 和 SD2 中发现了很强的相关性（分别为 0.99、0.89、0.84、0.89、0.90 和 0.95）。
